Fluorescence Detection of Polycyclic Aromatic Hydrocarbons by Parallel Factor Combined with Support Vector Machine

Shutao, 王书涛 Wang; Xing, 吴兴 Wu; Wenhao, 朱文浩 Zhu; Mingshan, 李明珊 Li

doi:10.3788/aos201939.0530002

Cited by 1 publication

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“…Set the initial value of the position and velocity for each particle in the population and then calculate the initial fitness value of each particle. The root mean square error is selected as the fitness function, and the expression is shown in Equation (6).…”

Section: Parameter Identification Of Static Nonlinear Sectionmentioning

confidence: 99%

“…In recent years, piezoelectric actuators (PEAs) have attracted much attention due to their advantages, such as a large output force, compact size, high resolution, and quick response. They have a wide range of potential applications in the fields of micro-manipulation [ 1 ], micromechanical manufacturing [ 2 ], super-precision processing [ 3 ], adaptive optical compensation [ 4 , 5 , 6 ], and robotics [ 7 , 8 , 9 , 10 ]. However, piezoelectric ceramic materials have hysteretic nonlinear characteristics of multi-valued mapping and frequency dependence [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Parameter Identification of Model for Piezoelectric Actuators

Dong

Guo

et al. 2023

Micromachines

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Piezoelectric actuators are widely used in high-precision positioning systems. The nonlinear characteristics of piezoelectric actuators, such as multi-valued mapping and frequency-dependent hysteresis, severely limit the advancement of the positioning system’s accuracy. Therefore, a particle swarm genetic hybrid parameter identification method is proposed by combining the directivity of the particle swarm optimization algorithm and the genetic random characteristics of the genetic algorithm. Thus, the global search and optimization abilities of the parameter identification approach are improved, and the problems, including the genetic algorithm’s poor local search capability and the particle swarm optimization algorithm’s ease of falling into local optimal solutions, are resolved. The nonlinear hysteretic model of piezoelectric actuators is established based on the hybrid parameter identification algorithm proposed in this paper. The output of the model of the piezoelectric actuator is in accordance with the real output obtained from the experiments, and the root mean square error is only 0.029423 μm. The experimental and simulation results show that the model of piezoelectric actuators established by the proposed identification method can describe the multi-valued mapping and frequency-dependent nonlinear hysteresis characteristics of piezoelectric actuators.

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Section: Parameter Identification Of Static Nonlinear Sectionmentioning

confidence: 99%